Observation-based user profiling and profile matching

ABSTRACT

Observation-based user profiling and profile matching are provided. The network behavior of users of a computer-implemented social network are observed and used for user profiling. By observing network behavior instead of necessarily relying on user self-reported data, accurate and objective user profiles can be formed; user profiling is accomplished based on the observed network behaviors with or without the knowledge of the user being profiled. The observed network behaviors can include the customization of a visual graphic, a media preference, a communication preference, or a selection of words from a word list. The user profiles can be with respect to a domain and two or more users can be matched based on their profiles with respect to the same domain. User ratings and profile updating based on the ratings are also provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/215,957 filed Jun. 30, 2008, now U.S. Pat. No. 8,156,064 which is acontinuation-in-part of U.S. patent application Ser. No. 11/999,249filed Dec. 3, 2007, which are both incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates generally to social networks. More particularly,the present invention relates to user profiling and profile matching ina computer-implemented social network.

BACKGROUND

Many Internet-based or web-based social networks are powerful tools toconnect people separated by potentially vast distances. Social networkscurrently allow users to build a user profile and connect to friendswhom the user may or may not already know from other social means. Someexisting social networks, particularly dating websites, offersuggestions to their users for possible matches with other users.Typically, these matches are suggested based on characteristics providedin the user profiles.

User matching found in existing web-based social networks, however,suffer from the fact that they rely on user self-reported data to builda user profile. For example, dating websites typically provide surveysfor their users to fill out. The surveyed users are then matched withsuggested counterparts based on the survey data. With user profilesstrictly reliant on self-reported data, users can easily provide aninaccurate portrayal of themselves. In other words, users are likely togame the system to attempt to look better or different than their actualselves. Since users are matched based on their profiles, inaccurate userprofiles would naturally lead to improper or imperfect matches.

Furthermore, the user surveys provided by existing social networkstypically ask the users to answer direct questions related to aparticular subject. Therefore, the surveys are only relevant to one or asmall number of topics or subjects and can be completely useless foruser profiling with respect to other topics. In this scenario,additional user surveys may be required when the social network attemptsto match users for other purposes. The additional surveys would be anannoyance to the users of the social network. For the example of datingwebsites, the user surveys generally contain questions related toparticular romantic interests of the user. Though these questions may berelevant to dating, they may be completely irrelevant for anotherpurpose, such as a community service event.

The present invention addresses at least the difficult problems of userprofiling and profile matching.

SUMMARY OF THE INVENTION

The present invention is directed to profiling and profile matchingbased on observed network behavior. An application server operates acomputer-implemented social network of users. One or more networkbehaviors of at least some of the users of the social network areobserved. A network behavior can include a customization of a visualgraphic by the observed user, a media preference of the observed user, acommunication preference of the observed user, or any combinationthereof. The observed network behaviors can be stored in a database.

Observed users are profiled based on the network behaviors, where theprofiling is with respect to one or more domains, such as a romanticinteraction, a transportation organization, an exercise goal, a fitnessgoal, a diet regimen, a smoking habit, an addiction, a cause, acommunity service, a physical activity, an emotional state, or anycombination thereof. Two or more of the profiled users can be matchedwith respect to the same domain based on the user profiles.

In a preferred embodiment, the user profiling includes associating oneor more scale factors for each of the observed network behaviors. Thescale factor associating is based on a relevance of an observed networkbehavior to the domain used in profiling. In an embodiment, a ratingfunction is also provided for rating the profile matching. The ratingcan be based on one or more user inputs of the matched profiled users, aparticipation data of the matched profiled users, or any combinationthereof. The ratings can be used to update the user profiles and/orprofile matches. In a preferred embodiment, the scale factors arerefined based on the ratings.

In an embodiment, an observed network behavior includes thecustomization of an avatar, including determining the appearance of theavatar. Another observed network behavior includes media preference,where the media preference is associated with a media content, a mediatype, a frequency of media playback or any combination thereof. Anobserved network behavior can also include a communication preference,including a communication method, a communication timing, acommunication frequency, or any combination thereof. In an embodiment, anetwork behavior also includes a selection of words from a word list bythe observed user, where the words are associated with a value, a need,a goal, or any combination thereof.

BRIEF DESCRIPTION OF THE FIGURES

The present invention together with its objectives and advantages willbe understood by reading the following description in conjunction withthe drawings, in which:

FIG. 1 shows an example of matching users U of a computer-implementedsocial network based on profiles P(D) from observed network behavior NBaccording to the present invention.

FIG. 2 shows a matrix of scale factors SF for domains D and networkbehaviors NB according to the present invention.

FIG. 3 shows an example function for customizing an avatar according tothe present invention.

FIG. 4 shows an example word selection as an example network behaviorfor observation according to the present invention.

FIG. 5 shows an example of a portable communication device where usernetwork behavior can be observed according to the present invention.

FIG. 6 shows an example of a rating function including user input andparticipation data according to the present invention.

FIG. 7 shows a flow chart including profiling, profile matching, andprofile updating according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Accurately profiling individuals can be a difficult task. Incomputer-implemented social networks, self-reported data are often usedto profile individuals and, possibly, match multiple individuals basedon the profiles. However, self-reported data can be highly inaccuratedue to their reliance on the objectivity of the reporting individuals.The present invention is directed to profiling and profile matchingbased on observed network behavior of users of a computer-implementedsocial network.

FIG. 1 shows an example of a computer-implemented social network ofusers U. An application server operates the social network over acommunication network, such as the Internet. In an embodiment, each userU of the social network is able to communicate, or otherwise interact,with at least another user U of the social network. In certainembodiments, each user has a list of friends who may also be users ofthe social network.

It is important to note that the users U of the social network canperform one or more network behaviors NB, including a customization of avisual graphic, a media preference, and a communication preference. Thenetwork behaviors NB can be observed and stored in a database. Thenetwork behavior NB is used to profile each of the observed users U. Ina preferred embodiment a profile P(D) is generated with respect to oneor more domains D. The domain D can be related to a romanticinteraction, a transportation organization, an exercise goal, a fitnessgoal, a diet regimen, an addiction, a smoking habit, a cause, acommunity service, a physical activity, an emotional state, or anycombination thereof. The profile P(D) of each user U is generated by aprofiling function based on the observed network behavior NB.

In a preferred embodiment, the profiles P(D) of the observed users U areused to match two or more compatible users with respect to the domain D.In FIG. 1, user 110 and user 120 both have profiles P(D) related to X.Due to this relation, a matching function can match user 110 and user120 with respect to the domain D. In an embodiment, the purpose ofprofile matching can be related to certain goals, needs, or interests ofthe users. For example, users can be matched to find a carpool partner,to find an exercise partner, to find a support group for diet, weightloss, smoking, or another addiction, or to find a cause or communityservice activity. In other embodiments, matched users can becommunicatively connected, such as by email, telephone, instantmessaging, video chatting, and/or in person. For computer-implementedsocial networks where each user has a friends list, matching can be usedto add friends to the friends list.

In a preferred embodiment of the present invention, the profilingfunction associates one or more scale factors for each of the observednetwork behaviors NB. The scale factors are associated based on arelevance of the observed network behavior NB to the domain D. FIG. 2shows a matrix of scale factors SF for the domains D and the networkbehaviors NB for one of the users. In particular, an element of thematrix SF_(ij) can be related to the relevance of NB_(i) with respect todomain D_(j). As shown in FIG. 2, a user profile P(D_(j)) with respectto domain D_(j) is generated based on the scale factors SF in the columnof domain D_(j).

A network behavior NB may be directly relevant, indirectly relevant,partially relevant, or irrelevant to a particular domain D. For example,music preferences may be important to profiling and profile matchingusers for a carpool, but not relevant to another domain. In contrast,values and ideals may be important to profiling users to join a cause,but not with matching users for a carpool. In another example,personality types, such as leader versus follower, risk taker versusconservative, may be relevant for user profiling in a support group forchanging lifestyle and behavior, such as a weight loss or smokingcessation program. In an embodiment, the magnitudes of the scale factorsSF represent the relevancy of the network behavior NB with respect to adomain. The use of observed network behaviors NB enables profiling andprofile matching for any number of domains D without necessarilyrequiring domain-specific input.

It is important to note that a network behavior is observed for apurpose of profiling one or more characteristics of a user withoutnecessarily relying on user-reported data relating to the samecharacteristics. By not relying on user-reported data, the user cannotalter or game his or her profile by providing dubious information usedin profiling. In fact, the user may not even be aware of how theobserved network behavior is used for profiling.

In a preferred embodiment, a network behavior can include acustomization or creation of a visual graphic, such as an avatar, anicon, or any visual representation. FIG. 3 shows an example customizingfunction 300 for users of a social network to customize an avatar 360.The customizing function 300 allows a user to adjust apparent physicaltraits of the avatar 360, such as height 310, weight 320, hair color330, and hair length 340. In an embodiment, the user is offered aselection of choices for the avatar look and clothing. Creating anavatar's look and feel can reveal user preferences, such as gender,gender-related preferences, style, and attitude, without explicitlyasking the user. Though the customizing function 300 shows a toggle 350for adjusting the properties of the physical traits, other methods ofcustomizing the appearance of the avatar 360 can be employed, such asdragging and dropping desired characteristics onto the avatar 360 orvisual graphic.

The observed network behavior can also include user media preference. Inan embodiment, the media preference is associated with media content,media type, frequency of media playback, or any combination thereof. Themedia content can include music, movies, television shows, and otheraudio-visual media. The media preferences can be extracted from themedia player operated by the user or by user-entry. In addition, one ormore behavioral characteristic, such as early adopter behavior oropenness to new experience, can be determined based on timing, source,and means of media purchases. For example, information relating towhether or not the media was discovered originally by the user orthrough friends of the user can be used to measure attributes about theuser. Similarly, information relating to the time after release of themedia may be pertinent to certain domains and behavioralcharacteristics.

In an embodiment, the observed network behavior includes one or morecommunication preferences of the user. The communication preferencesinclude communication methods, such as email, telephone, voice mail, andtext messaging, communication timing, communication frequency, or anycombination thereof. In an embodiment, communication timing andfrequency includes when and how often each communication method is used,the length of time to respond to voice mails, emails, and other forms ofmessaging, and the length of time messages are left in the mail box. Theabove communication preferences can also be used to determine one ormore user behavioral patterns and personality types. For example, userpreference and frequency for video chatting, text messaging, email, ortelephone can help to predict the technological acumen of a user. Thetiming of communications can also help to determine if the usergenerally functions in the early morning or late at night.

In another embodiment of the present invention, the observed networkbehaviors include a selection of words by a user from a word list. Thewords available for selection can be associated with values, needs,goals, or any combination thereof. FIG. 4 shows an example of lists ofwords 410 available for selection to describe properties of a selectinguser. In a preferred embodiment, word selection occurs when a user dragsone or more words from the word list 410 to an icon, such as a candle430. Other methods of selecting words, such as text boxes, can also beemployed. Optionally, a user can also add new words 440 to the wordlists.

An embodiment of the present invention is directed to observing networkbehaviors associated with a handheld communication device and at leastpartially profiling users based on the observed network behaviors on thehandheld communication device. FIG. 5 shows an example of a handheldcommunication device 500 according to the present invention. Thehandheld communication device 500 can be any portable device capable ofcommunicating with the network, including the application serveroperating the computer-implemented social network. For example, ahandheld communication device 500 can include a mobile phone 510, anetwork-integrated mobile phone, such as the iPhone by Apple Computers(Cupertino, Calif., USA), a personal digital assistant, a laptopcomputer, a medical device, a wristwatch, an exercise device, a healthmonitor 570, a media player, a music player 540, a movie player 550, anmp3 player, or any combination thereof.

In an embodiment, network behaviors on the handheld communication device500 to be observed include the above-described communicationpreferences, such as communication methods, timing, and frequency.Communication methods can include phone 510, text messaging 520, andelectronic mail 530. In an embodiment with a mobile phone handheldcommunication device 500, ring tone selection and the assignment of ringtones to individuals can also be observed and used for user profilingand profile matching.

In certain embodiments, the handheld communication device 500 can alsoinclude media playing capabilities 540-550. The observed networkbehaviors on the handheld communication device 500 can include the mediapreferences described above. In particular, user media preferences areobserved from media content stored and/or media playback by the handheldcommunication device 500.

Many portable communication devices, such as mobile phones, havelocation-aware, activity-aware 560, and/or motion-aware technology. Inan embodiment, network behaviors based on location-awareness,activity-awareness, and/or motion-awareness of the user carrying theportable communication device can be observed and used for userprofiling and profile matching. In particular, a portable communicationdevice 500 can include an accelerometer, a GPS device, a heart monitor570, or any device capable of detecting user activity. User activity canbe observed and communicated to a profiling function for profiling theuser. For example, the speed and distance a user runs and the mediapreferences of the user while running or undergoing an activity can beobserved and used to profile the user. The profiles can be used for userprofile matching. Further descriptions of example portable communicationdevices having activity-detection and network communication capabilitiesusable in embodiments of the present invention can be found in U.S.patent application Ser. No. 11/999,249 filed on Dec. 3, 2007, which ishereby incorporated by reference in its entirety.

It is important to note that in a preferred embodiment, the userprofiles are not static. In particular, the scale factors are updatable;therefore the profiles with respect to one or more domains can change.Changes to user profiles and/or scale factors can be caused byadditional observed network behaviors or changes to past networkbehaviors. In addition, changes to user profiles can occur due toupdates to the relevancy of one or more network behaviors with respectto a domain. In fact, the relevancy updates of the network behaviors cancause changes to user profiles and/or scale factors with or without anyadditions or changes to the network behaviors of the observed users. Therelevance of a network behavior with respect to a domain can be updatedby network activity of one or more users of the social network, or bycumulative activity of some or all of the users of the social network.

In a preferred embodiment, a rating function is provided for users torate the quality of the profiling and/or the profile matching. Anexample rating function 600 is shown in FIG. 6. In an embodiment, therating function 600 can be accessed on a handheld communication device,In particular, the ratings can be based on one or more user inputs anduser surveys. For example, a user can be matched with one or more otherusers with respect to activity X, such as for carpooling. The ratingfunction 600 allows the user to rate his or her compatibility with thematched users 610 or rate the overall profile matching 640. Ratings caninclude alphanumeric metrics, a metric of symbols, such as a number ofstars 650, or any other type of rating metric.

The rating function can also include participation data 620. Theparticipation data can be derived from user input 630. Alternatively oradditionally, the participation data can be observed without user input.In an embodiment, the cancelation of the participation of matched usersfor an activity or the churn rate of matched users can be used to ratethe profile matching.

It is important to note that the ratings can be used to update theprofiles of the profiled users. In particular, the scale factors withrespect to one or more domains can be raised or lowered based on thesuccess of the profile matching as determined by the ratings. FIG. 7shows a flow chart for an embodiment of the present invention withprofile updating, including observing one or more network behaviors ofusers 710, associating a scale factor to each of the observed networkbehavior with respect to a domain 720, profiling each of the observedusers based on the scale factors 730, matching users having compatibleprofiles 740, rate profile matching 750, and updating the scale factorsbased on the ratings 760. The updated scale factors can be used toupdate the user profiles 770. The ratings can also be used to measurethe relevance of a network behavior with respect to one or more domains.

As one of ordinary skill in the art will appreciate, various changes,substitutions, and alterations could be made or otherwise implementedwithout departing from the principles of the present invention, e.g. theInternet can be replaced by a local area network (LAN), a wide areanetwork (WAN), or any other communication network and a domain caninclude any activity, goal, need, and/or interest. Accordingly, thescope of the invention should be determined by the following claims andtheir legal equivalents.

What is claimed is:
 1. A method of profile matching in acomputer-implemented social network of a plurality of users, said methodcomprising: (a) observing network behaviors of at least some of saidplurality of users of said social network, called observed users,wherein said network behaviors of each of said observed users comprise aselection of words from a word list by one of said observed users, andwherein said words are associated with a value, a need, a goal, or anycombination thereof; (b) profiling at least some of said observed usersbased on said observed network behaviors of the same of said observedusers, wherein said profiling is with respect to one or more domains,wherein said profiling comprises associating one or more scale factorsfor each of said observed network behaviors based on a relevance of thesame of said observed network behaviors to said domain used in saidprofiling; (c) matching two or more of said profiled users with respectto the same of said domains, wherein said matching is based on saidprofiling; (d) rating said profile matching based on one or more userinputs of said matched profiled users, a participation data of saidmatched profiled users, or any combination thereof; and (e) updatingsaid profiling by refining said scale factors based on said rating.